Anode heel effect: Does it impact image quality in digital radiography? A systematic literature review

Kusk, Martin Weber; Jensen, Josefine Magdalene; Gram, Emma Henneberg; Nielsen, Jesper; Precht, Helle

doi:10.1016/j.radi.2021.02.014

Cited by 9 publications

(9 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Photons emitted towards the anode side (region 2) are more attenuated by the anode itself than those emitted towards the cathode side (region 3). This effect can affect raw pixel values in digital radiography, for example [24].…”

Section: Resultsmentioning

confidence: 99%

Validation and study of different parameters in the simulation of diagnostic X-ray spectra using the MCNPX code

et al. 2023

View full text Add to dashboard Cite

In radiology, knowing the X-ray spectrum characteristics makes it possible to estimate the absorbed dose in the patient and to improve image quality. In this study, an X-ray generator was proposed using the MCNPX code and to validate it, the simulated spectrum was compared to the data provided from AAPM Task Group 195, which resulted in a percentage difference of 8.7%. Furthermore, several X-ray spectra were generated and compared to the spectra obtained from commercially available softwares as xpecgen and SpekCalc. The percentage differences were of the order of 13% in comparison with SpekCalc and 8% with xpecgen. The major differences obtained between those spectra were concentrated in the region of characteristic peaks, independently if variations in electron beam energy, target angle or filtration thickness were performed.

show abstract

Section: Resultsmentioning

confidence: 99%

Validation and study of different parameters in the simulation of diagnostic X-ray spectra using the MCNPX code

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The gray value profiles g̃ i , k were corrected for the anode heel effect with the procedure described in SI3, resulting in the corrected gray value profiles g i , k . A unique gray value of the gel at a given drying stage was calculated by averaging g i , k along the slice number i with eq G k = 1 Ṽ k ∑ i = 0 N g i , k · A i , k where G k is called the “global gray value” and N is the total number of slices.…”

Section: Methodsmentioning

confidence: 99%

“…The gray value profiles g̃ i , k were corrected for the anode heel effect with the procedure described in SI3, resulting in the corrected gray value profiles g i , k . A unique gray value of the gel at a given drying stage was calculated by averaging g i , k along the slice number i with eq where G k is called the “global gray value” and N is the total number of slices.…”

Section: Methodsmentioning

confidence: 99%

In Operando μCT Imaging of Silylated Silica Aerogels during Ambient Pressure Drying and Spring-Back

Gonthier,

Rilling,

Scoppola

et al. 2023

Chem. Mater.

View full text Add to dashboard Cite

“…Another phenomenon that further influences the distribution of photon intensity across radiographic images is the anode heel effect [31]. In most X-ray tubes, the anode (the component that emits X-rays) is positioned at an angle, leading to the generation of X-ray beam intensity distribution that is not uniform when the X-ray photons are sensed by the detector.…”

Section: Intensity Distribution: Real Vs Simulated Imagesmentioning

confidence: 99%

Towards Enhancing Automated Defect Recognition (ADR) in Digital X-ray Radiography Applications: Synthesizing Training Data through X-ray Intensity Distribution Modeling for Deep Learning Algorithms

Hena,

Wei,

Perron

et al. 2023

Information

View full text Add to dashboard Cite

Industrial radiography is a pivotal non-destructive testing (NDT) method that ensures quality and safety in a wide range of industrial sectors. Conventional human-based approaches, however, are prone to challenges in defect detection accuracy and efficiency, primarily due to the high inspection demand from manufacturing industries with high production throughput. To solve this challenge, numerous computer-based alternatives have been developed, including Automated Defect Recognition (ADR) using deep learning algorithms. At the core of training, these algorithms demand large volumes of data that should be representative of real-world cases. However, the availability of digital X-ray radiography data for open research is limited by non-disclosure contractual terms in the industry. This study presents a pipeline that is capable of modeling synthetic images based on statistical information acquired from X-ray intensity distribution from real digital X-ray radiography images. Through meticulous analysis of the intensity distribution in digital X-ray images, the unique statistical patterns associated with the exposure conditions used during image acquisition, type of component, thickness variations, beam divergence, anode heel effect, etc., are extracted. The realized synthetic images were utilized to train deep learning models, yielding an impressive model performance with a mean intersection over union (IoU) of 0.93 and a mean dice coefficient of 0.96 on real unseen digital X-ray radiography images. This methodology is scalable and adaptable, making it suitable for diverse industrial applications.

show abstract

Anode heel effect: Does it impact image quality in digital radiography? A systematic literature review

Cited by 9 publications

References 14 publications

Validation and study of different parameters in the simulation of diagnostic X-ray spectra using the MCNPX code

Validation and study of different parameters in the simulation of diagnostic X-ray spectra using the MCNPX code

In Operando μCT Imaging of Silylated Silica Aerogels during Ambient Pressure Drying and Spring-Back

Towards Enhancing Automated Defect Recognition (ADR) in Digital X-ray Radiography Applications: Synthesizing Training Data through X-ray Intensity Distribution Modeling for Deep Learning Algorithms

Contact Info

Product

Resources

About